System

Abstract

To provide a system according to an embodiment that performs an online price comparison or expenditure management, and an expiration date management of a point or a coupon efficiently.SOLUTION: A system according to an embodiment comprises: a collection unit; an analysis unit; a comparison table display unit; an acquisition unit; a display unit; a notification unit; and a proposal unit. The collection unit is configured to collect a price or discount information from various sites. The analysis unit is configured to analyze information collected by the collection unit, and generate a comparison table. The comparison table display unit is configured to display the comparison table generated by the analysis unit. The acquisition unit is configured to acquire information from a mail upon purchasing the goods. The display unit is configured to display an expenditure every month by a category and visually on the basis of the information acquired by the acquisition unit. The notification unit is configured to monitor a budget upper limit amount set by a user, and alert when the amount exceeds. The proposal unit is configured to propose an expiration date management of a point or a coupon, and a utilization method thereof.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, online price comparisons, expense management, and point and coupon expiration date management were time-consuming and difficult to do efficiently.

[0005] The system according to the embodiment aims to efficiently perform online price comparisons, expense management, and expiration date management of points and coupons. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a comparison table display unit, an acquisition unit, a display unit, a notification unit, and a suggestion unit. The collection unit collects price or discount information from various sites. The analysis unit analyzes the information collected by the collection unit and generates a comparison table. The comparison table display unit displays the comparison table generated by the analysis unit. The acquisition unit acquires information from an email sent at the time of product purchase. The display unit visually displays monthly expenses in categories based on the information acquired by the acquisition unit. The notification unit monitors the budget upper limit set by the user and notifies the user by an alert if the upper limit is exceeded. The suggestion unit suggests methods for managing and utilizing the expiration dates of points or coupons. Effect of the Invention

[0007] The system according to the embodiment can efficiently perform online price comparisons, expense management, and expiration date management of points and coupons. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8]FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Fig. 2, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific process program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The online shopping support system according to the embodiment of the present invention is a system that collects price and discount information from various sites, automatically generates a comparison table using AI, and tells the lowest price. This system acquires information from emails when purchasing products, and visually displays monthly expenses in categories. In addition, the system monitors the budget upper limit set by the user, and notifies the user by alert when the upper limit is exceeded. In addition, the system also manages the expiration dates of points and coupons and suggests how to use them. For example, the collection unit collects product information from multiple shopping sites on the Internet. The collected information is analyzed by AI, and a comparison table is generated. The analysis unit analyzes the collected information, compares prices and discount information, and identifies the lowest price. The generated comparison table is displayed to the user by the comparison table display unit. This allows the user to easily find the lowest-priced product. Next, the acquisition unit accesses the user's email account and acquires product information from purchase confirmation emails, etc. The acquired information is analyzed by AI, and monthly expenses are categorized. The categorized expenses are visually displayed. The display unit displays expenses by category in graphs, charts, etc., allowing the user to grasp the situation of expenses at a glance. Furthermore, the notification unit monitors the budget upper limit set by the user, and issues an alert when the expenditure exceeds the upper limit. This allows the user to manage so as not to exceed the budget. Finally, the suggestion unit manages the expiration date of points and coupons held by the user, and suggests the optimal way to use them. For example, when the expiration date of points is approaching, the suggestion unit suggests the optimal product for using points, or suggests stores and products where coupons can be used. This allows the user to make the most of points and coupons without waste. This allows the online shopping support system to find the lowest price, manage expenditures, and make effective use of points and coupons when a user purchases products online.

[0029] The online shopping support system according to the embodiment includes a collection unit, an analysis unit, a comparison table display unit, an acquisition unit, a display unit, a notification unit, and a suggestion unit. The collection unit collects price or discount information from various sites. The collection unit collects product information from, for example, a plurality of shopping sites on the Internet. The collected information is analyzed by AI, and a comparison table is generated. The analysis unit analyzes the collected information and compares the price and discount information to identify the lowest price. For example, the analysis unit identifies the cheapest price based on the collected price information of the product. The analysis unit can also analyze the discount information and identify the most advantageous discount. The generated comparison table is displayed to the user by the comparison table display unit. For example, the comparison table display unit displays the generated comparison table in a table format. The comparison table display unit can also display in a graph format. The acquisition unit acquires information from an email at the time of purchasing the product. For example, the acquisition unit accesses the user's email account and acquires product information from a purchase confirmation email or the like. The acquired information is analyzed by AI, and monthly expenses are categorized. The display unit displays the expenses by category in a graph or chart, so that the user can grasp the situation of the expenses at a glance. For example, the display unit displays the expenses in a pie chart. The display unit can also display the expenses in a bar graph. The notification unit monitors the budget upper limit set by the user, and notifies the user by an alert when the expenses exceed the upper limit. For example, the notification unit issues a pop-up notification when the budget upper limit is exceeded. The notification unit can also issue an email notification. The suggestion unit manages the expiration date of points and coupons held by the user, and suggests an optimal way to use the points. For example, the suggestion unit suggests an optimal product for using the points when the expiration date of the points is approaching. The suggestion unit can also suggest stores and products for which coupons can be used. As a result, the online shopping support system according to the embodiment can find the lowest price, manage expenses, and effectively use points and coupons when purchasing products online by the user.

[0030] The collection unit collects price or discount information from various sites. The collection unit collects product information from, for example, multiple shopping sites on the Internet. Specifically, the collection unit uses web scraping technology to analyze the HTML structure of each shopping site and extract data such as product name, price, discount information, and stock status. This allows the collection unit to obtain the latest price information and discount information in real time. The collection unit can also use an API to obtain data directly from the shopping site. For example, the API provided by a specific shopping site is used to periodically obtain product information and store it in a database. Furthermore, the collection unit can preferentially collect information on specific products or categories specified by the user. This allows the user to quickly grasp price fluctuations and discount information for products in which the user is interested. The collected information is stored in a database and is ready to be analyzed by the analysis unit.

[0031] The analysis unit analyzes the collected information and compares prices and discount information to identify the lowest price. Specifically, the analysis unit uses an AI algorithm to analyze the collected data and compares the price and discount information of each product. For example, the AI ​​uses a machine learning model to analyze the price history of each product and identify price trends and fluctuation patterns. In addition, the AI ​​uses natural language processing technology to analyze the text of the discount information and extract the discount rate and application conditions. This allows the analysis unit to identify the cheapest price and the most advantageous discount. Furthermore, the analysis unit can also recommend the most suitable product individually, taking into account the user's purchase history and preferences. For example, based on the trends of products purchased in the past, the analysis unit identifies products that the user is likely to be interested in and provides the lowest price and discount information for those products. Based on this information, the analysis unit presents the most advantageous purchase options for the user.

[0032] The generated comparison table is displayed to the user by the comparison table display unit. Specifically, the comparison table display unit displays the generated comparison table in a table format. For example, information such as the price, discount rate, sales site, and stock status of each product is displayed in a table format, allowing the user to easily compare. The comparison table display unit can also display in a graph format. For example, price fluctuations are displayed in a line graph, allowing the user to visually check past price history. Furthermore, the comparison table display unit provides a filter function that can be customized by the user, allowing the user to narrow down products based on specific conditions. For example, the user can narrow down the products displayed by specifying conditions such as a price range, discount rate, and sales site. This allows the user to efficiently find products that meet his or her needs.

[0033] The acquisition unit acquires information from the email sent when the product is purchased. Specifically, the acquisition unit accesses the user's email account and acquires product information from the purchase confirmation email, etc. For example, the acquisition unit connects to the user's email server using IMAP or POP3 protocol and searches for the purchase confirmation email. The purchase confirmation email contains information such as the product name, purchase price, purchase date, and sales site, and the acquisition unit extracts this information and stores it in a database. Furthermore, the acquisition unit uses AI to analyze the contents of the email and automatically extracts important information. For example, natural language processing technology is used to identify the product name and price from the email body and register them in a database. As a result, the acquisition unit accurately grasps the user's purchase history and makes it available to the analysis unit and display unit.

[0034] The display unit displays the expenses for each category in a graph or chart, so that the user can grasp the situation of the expenses at a glance. Specifically, the display unit displays the expenses for each category in a pie chart or a bar graph based on the collected purchase information. For example, the expenses are totaled for each category such as food, clothing, and home appliances, and the percentage is displayed in a pie chart. In addition, the monthly expenses are displayed in a bar graph, so that the transition of expenses can be visually confirmed. Furthermore, the display unit provides a filter function that can be customized by the user, and can display expenses narrowed down to a specific period or category. For example, by displaying expenses for the past three months and expenses for a specific category, the user can grasp the detailed situation of the expenses. In this way, the display unit supports the user in efficiently managing expenses.

[0035] The notification unit monitors the budget upper limit set by the user, and notifies the user by an alert when the expenditure exceeds the upper limit. Specifically, the notification unit saves the budget upper limit set by the user in a database and monitors expenditure information in real time. For example, the user sets a monthly budget upper limit, and issues an alert when the expenditure approaches the upper limit. The notification unit sends the alert to the user using multiple notification means, such as a pop-up notification, an email notification, or a push notification on a smartphone. Furthermore, the notification unit can also suggest specific saving methods or alternatives when the expenditure exceeds the budget upper limit. For example, the notification unit supports the user to continue shopping within the budget by providing money saving techniques for suppressing expenditure and discount information. In this way, the notification unit prevents the user from spending more than the budget and enables the user to make planned shopping.

[0036] The suggestion unit manages the expiration dates of points and coupons held by the user and suggests optimal ways to use them. Specifically, the suggestion unit stores information on points and coupons held by the user in a database and monitors the expiration dates. For example, when the expiration date of points is approaching, the suggestion unit suggests optimal products and services for using points. It also identifies stores and products for which coupons can be used and notifies the user. Furthermore, the suggestion unit uses AI to analyze the user's purchase history and preferences and makes optimal suggestions individually. For example, it identifies products that the user may be interested in based on past purchase history and suggests coupons that can be applied to those products. In this way, the suggestion unit supports the user to make effective use of points and coupons and enjoy shopping at a better price.

[0037] The collection unit can collect product information from multiple shopping sites on the Internet. The collection unit, for example, collects product information from multiple shopping sites on the Internet. For example, the collection unit acquires information from an e-commerce site, a price comparison site, and the like. The collected information is analyzed by AI, and a comparison table is generated. In this way, a wide range of information can be obtained by collecting product information from multiple shopping sites. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can collect product information from multiple shopping sites on the Internet, input the information to AI, and the AI ​​can perform analysis.

[0038] The analysis unit can analyze the collected information and compare prices and discount information to identify the lowest price. The analysis unit can analyze the collected information and compare prices and discount information to identify the lowest price. For example, the analysis unit can identify the lowest price based on the collected price information of the product. The analysis unit can also analyze the discount information to identify the most advantageous discount. For example, the analysis unit inputs the price information of the product into an AI, which identifies the lowest price. This allows the user to find the most advantageous product by comparing the price and discount information to identify the lowest price. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the analysis unit can input the collected information into an AI, which analyzes the price and discount information to identify the lowest price.

[0039] The acquisition unit can access the user's email account and acquire product information from a purchase confirmation email or the like. The acquisition unit can access the user's email account and acquire product information from a purchase confirmation email or the like. For example, the acquisition unit can access the user's email account and acquire product information from a purchase confirmation email, a shipping notification email, or the like. The acquired information is analyzed by AI, and monthly expenses are categorized. In this way, by acquiring product information from the user's email account, the purchase history can be automatically managed. Some or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can access the user's email account, input the acquired information into AI, and the AI ​​can perform analysis.

[0040] The display unit can display the expenses by category in a graph or chart, allowing the user to grasp the status of the expenses at a glance. The display unit can display the expenses by category in a graph or chart, allowing the user to grasp the status of the expenses at a glance. For example, the display unit displays the expenses in a pie chart. The display unit can also display the expenses in a bar graph. This allows the user to easily manage the expenses by visually displaying the status of the expenses. Some or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input the expense data to AI, which can generate graphs and charts.

[0041] The notification unit can monitor the budget upper limit set by the user and notify by an alert when the expenditure exceeds the upper limit. The notification unit can monitor the budget upper limit set by the user and notify by an alert when the expenditure exceeds the upper limit. For example, the notification unit issues a pop-up notification when the budget upper limit is exceeded. The notification unit can also issue an email notification. This allows the user to manage the budget so that it does not exceed the budget by notifying by an alert when the budget upper limit is exceeded. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI or may be performed without using AI. For example, the notification unit can input expenditure data into AI, which monitors the budget upper limit and notifies by an alert when it is exceeded.

[0042] The suggestion unit can manage the expiration dates of points and coupons held by the user and suggest appropriate ways to use them. The suggestion unit can manage the expiration dates of points and coupons held by the user and suggest appropriate ways to use them. For example, when the expiration date of points is approaching, the suggestion unit can suggest the optimal product for using points. The suggestion unit can also suggest stores and products where coupons can be used. In this way, by managing the expiration dates of points and coupons and suggesting the optimal ways to use them, the user can use points and coupons without waste. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data of points and coupons into AI, which can suggest the optimal ways to use them.

[0043] The collection unit can analyze the user's past purchase history and select the optimal collection method. The collection unit analyzes the user's past purchase history and selects the optimal collection method. For example, the collection unit preferentially collects information from sites that the user has frequently used in the past. The collection unit can also preferentially collect product information of a specific category from the user's past purchase history. Furthermore, the collection unit can also analyze the user's past purchase history and collect information from the most reliable site. In this way, the optimal collection method can be selected by analyzing the user's past purchase history. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's purchase history data into AI, which can select the optimal collection method.

[0044] The collection unit can perform filtering based on the user's current purchasing willingness at the time of collection. The collection unit performs filtering based on the user's current purchasing willingness at the time of collection. For example, when the user shows a high purchasing willingness, the collection unit prioritizes collecting the latest discount information. In addition, when the user shows a low purchasing willingness, the collection unit can also collect general price information. Furthermore, the collection unit can filter and collect product information of a specific brand or category according to the user's purchasing willingness. This makes it possible to collect more appropriate information by filtering information based on the user's purchasing willingness. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's purchasing willingness data into AI, and the AI ​​can perform filtering.

[0045] The collection unit can preferentially collect highly relevant information in consideration of the geographical location information of the user at the time of collection. The collection unit preferentially collects highly relevant information in consideration of the geographical location information of the user at the time of collection. For example, the collection unit preferentially collects information of nearby stores based on the user's current location. The collection unit can also preferentially collect information of deliverable products in consideration of the geographical location information of the user. Furthermore, the collection unit can also preferentially collect area-limited discount information based on the geographical location information of the user. In this way, highly relevant information can be preferentially collected by considering the geographical location information of the user. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to AI, and the AI ​​can preferentially collect highly relevant information.

[0046] The collection unit can analyze the user's social media activity at the time of collection and collect related information. The collection unit can analyze the user's social media activity at the time of collection and collect related information. For example, the collection unit preferentially collects product information in which the user has shown interest on social media. The collection unit can also collect related discount information from the user's social media activity. Furthermore, the collection unit can also collect product information purchased by the user's followers and friends on social media. In this way, related information can be collected by analyzing the user's social media activity. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the user's social media data into AI, and the AI ​​can collect related information.

[0047] The analysis unit can adjust the level of detail of the analysis based on the importance of the product during the analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the product during the analysis. For example, the analysis unit performs a detailed analysis for an expensive product. The analysis unit can also perform a simplified analysis for a low-priced product. Furthermore, the analysis unit can perform a detailed analysis for a product in which the user is particularly interested. As a result, by adjusting the level of detail of the analysis based on the importance of the product, a more appropriate analysis result can be obtained. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the importance data of the product to an AI, which can adjust the level of detail of the analysis.

[0048] The analysis unit can apply different analysis algorithms depending on the category of the product during analysis. The analysis unit applies different analysis algorithms depending on the category of the product during analysis. For example, in the case of electronic devices, the analysis unit performs analysis that emphasizes technical specifications. In addition, in the case of clothing, the analysis unit can also perform analysis that emphasizes design and materials. Furthermore, in the case of food, the analysis unit can also perform analysis that emphasizes ingredients and expiration dates. In this way, by applying different analysis algorithms depending on the category of the product, more appropriate analysis results can be obtained. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input product category data to AI, and the AI ​​can apply different analysis algorithms.

[0049] The analysis unit can determine the priority of the analysis based on the submission time of the product during the analysis. The analysis unit determines the priority of the analysis based on the submission time of the product during the analysis. For example, the analysis unit performs analysis preferentially when a new product is released. The analysis unit can also prioritize the analysis of products during a sale period. Furthermore, the analysis unit can prioritize the analysis of products that the user is considering purchasing within a specific deadline. In this way, by determining the priority of the analysis based on the submission time of the product, more appropriate analysis results can be obtained. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the submission time data of the product to AI, and the AI ​​can determine the priority of the analysis.

[0050] The analysis unit can adjust the order of analysis based on the relevance of the products during analysis. The analysis unit adjusts the order of analysis based on the relevance of the products during analysis. For example, when the user is comparing multiple products, the analysis unit prioritizes the analysis of highly related products. The analysis unit can also prioritize the analysis of products in the same category. Furthermore, the analysis unit can also prioritize the analysis of products that are highly related to products purchased by the user in the past. This makes it possible to obtain more appropriate analysis results by adjusting the order of analysis based on the relevance of the products. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input product relevance data to AI, and the AI ​​can adjust the order of analysis.

[0051] The comparison table display unit can select the optimal display method by referring to the user's past browsing history when displaying the comparison table. The comparison table display unit can select the optimal display method by referring to the user's past browsing history when displaying the comparison table. For example, the comparison table display unit preferentially displays related information based on product information previously viewed by the user. The comparison table display unit can also preferentially display product information of a specific category from the user's past browsing history. Furthermore, the comparison table display unit can analyze the user's past browsing history and preferentially display information of the greatest interest. This allows the optimal display method to be selected by referring to the user's past browsing history. A part or all of the above-mentioned processing in the comparison table display unit may be performed, for example, using AI, or may be performed without using AI. For example, the comparison table display unit can input the user's browsing history data into AI, which can select the optimal display method.

[0052] The comparison table display unit can apply different display formats for each product category when displaying the comparison table. The comparison table display unit can apply different display formats for each product category when displaying the comparison table. For example, in the case of electronic devices, the comparison table display unit provides a display format that emphasizes technical specifications. In addition, in the case of clothing, the comparison table display unit can also provide a display format that emphasizes design and materials. Furthermore, in the case of food, the comparison table display unit can also provide a display format that emphasizes ingredients and expiration dates. This allows a more appropriate display method to be provided by applying different display formats for each product category. Part or all of the above-mentioned processing in the comparison table display unit may be performed, for example, using AI, or may be performed without using AI. For example, the comparison table display unit can input product category data into AI, and the AI ​​can apply different display formats.

[0053] The comparison table display unit can select the optimal display method in consideration of the device information of the user when displaying the comparison table. The comparison table display unit selects the optimal display method in consideration of the device information of the user when displaying the comparison table. For example, when the user is using a smartphone, the comparison table display unit provides a display method that matches the screen size. In addition, when the user is using a tablet, the comparison table display unit can also provide a display method optimized for a large screen. Furthermore, when the user is using a smart watch, the comparison table display unit can also provide a display method that is simple and highly visible. This makes it possible to select the optimal display method by considering the device information of the user. Part or all of the above-mentioned processing in the comparison table display unit may be performed, for example, using AI, or may be performed without using AI. For example, the comparison table display unit can input the device information of the user into AI, and the AI ​​can select the optimal display method.

[0054] The comparison table display unit can display highly relevant information preferentially in consideration of the user's geographical location information when displaying the comparison table. The comparison table display unit displays highly relevant information preferentially in consideration of the user's geographical location information when displaying the comparison table. For example, the comparison table display unit preferentially displays information of nearby stores based on the user's current location. The comparison table display unit can also preferentially display information of products that can be delivered in consideration of the user's geographical location information. Furthermore, the comparison table display unit can also preferentially display area-limited discount information based on the user's geographical location information. This makes it possible to preferentially display highly relevant information by considering the user's geographical location information. A part or all of the above-mentioned processing in the comparison table display unit may be performed using, for example, AI, or may be performed without using AI. For example, the comparison table display unit can input the user's geographical location information into AI, and the AI ​​can display highly relevant information preferentially.

[0055] The acquisition unit can analyze the user's past email history and select the optimal acquisition method. The acquisition unit analyzes the user's past email history and selects the optimal acquisition method. For example, the acquisition unit preferentially acquires information from emails that the user frequently received in the past. The acquisition unit can also preferentially acquire information of a specific category from the user's past email history. Furthermore, the acquisition unit can analyze the user's past email history and preferentially acquire the most important information. In this way, the optimal acquisition method can be selected by analyzing the user's past email history. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit inputs the user's email history data into AI, and the AI ​​can select the optimal acquisition method.

[0056] The acquisition unit can perform filtering based on the user's current purchasing willingness when acquiring information from the email. The acquisition unit performs filtering based on the user's current purchasing willingness when acquiring information from the email. For example, when the user shows a high purchasing willingness, the acquisition unit prioritizes acquiring information from the latest purchase confirmation email. In addition, when the user shows a low purchasing willingness, the acquisition unit can also acquire general information. Furthermore, the acquisition unit can filter and acquire information on a specific brand or category according to the user's purchasing willingness. This makes it possible to acquire more appropriate information by filtering information based on the user's purchasing willingness. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input the user's purchasing willingness data into AI, and the AI ​​can perform filtering.

[0057] The acquisition unit can preferentially acquire highly relevant information in consideration of the geographical location information of the user when acquiring information from the email. The acquisition unit preferentially acquires highly relevant information in consideration of the geographical location information of the user when acquiring information from the email. For example, the acquisition unit preferentially acquires emails from nearby stores based on the user's current location. The acquisition unit can also preferentially acquire information on deliverable products in consideration of the geographical location information of the user. Furthermore, the acquisition unit can also preferentially acquire area-limited discount information based on the geographical location information of the user. In this way, highly relevant information can be preferentially acquired by considering the geographical location information of the user. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input the geographical location information of the user to AI, and the AI ​​can preferentially acquire highly relevant information.

[0058] The acquisition unit can analyze the user's social media activity and acquire related information when acquiring information from the email. The acquisition unit can analyze the user's social media activity and acquire related information when acquiring information from the email. For example, the acquisition unit preferentially acquires product information in which the user has shown interest on social media. The acquisition unit can also acquire related discount information from the user's social media activity. Furthermore, the acquisition unit can also acquire product information purchased by the user's followers and friends on social media. In this way, related information can be acquired by analyzing the user's social media activity. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input the user's social media data into AI, and the AI ​​can acquire related information.

[0059] The display unit can select the optimal display method by referring to the user's past expenditure history when displaying the expenditure. The display unit selects the optimal display method by referring to the user's past expenditure history when displaying the expenditure. For example, the display unit preferentially displays categories in which the user has frequently spent in the past. The display unit can also preferentially display expenditures for a specific period from the user's past expenditure history. Furthermore, the display unit can analyze the user's past expenditure history and preferentially display information of the greatest interest. This allows the optimal display method to be selected by referring to the user's past expenditure history. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit inputs the user's expenditure history data into AI, which can select the optimal display method.

[0060] The display unit can apply different display formats for each category when displaying expenses. The display unit can apply different display formats for each category when displaying expenses. For example, in the case of food, the display unit provides a display format that emphasizes ingredients and expiration dates. In addition, in the case of clothing, the display unit can also provide a display format that emphasizes design and materials. Furthermore, in the case of electronic devices, the display unit can also provide a display format that emphasizes technical specifications. In this way, by applying different display formats for each category, a more appropriate display method can be provided. Part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input category data to AI, and the AI ​​can apply different display formats.

[0061] The display unit can select the optimal display method in consideration of the user's device information when displaying expenses. The display unit selects the optimal display method in consideration of the user's device information when displaying expenses. For example, when the user is using a smartphone, the display unit provides a display method that matches the screen size. In addition, when the user is using a tablet, the display unit can also provide a display method optimized for a large screen. Furthermore, when the user is using a smart watch, the display unit can also provide a simple and highly visible display method. This allows the optimal display method to be selected by considering the user's device information. Part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit inputs the user's device information into AI, and the AI ​​can select the optimal display method.

[0062] The display unit can display highly relevant information preferentially in consideration of the geographical location information of the user when displaying expenses. The display unit displays highly relevant information preferentially in consideration of the geographical location information of the user when displaying expenses. For example, the display unit preferentially displays expense information of nearby stores based on the user's current location. The display unit can also preferentially display expense information of deliverable products in consideration of the geographical location information of the user. Furthermore, the display unit can also preferentially display area-limited discount information based on the geographical location information of the user. This makes it possible to preferentially display highly relevant information by considering the geographical location information of the user. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input the geographical location information of the user to AI, and the AI ​​can display highly relevant information preferentially.

[0063] The notification unit can select the optimal notification method by referring to the user's past budget overrun history at the time of alert notification. The notification unit can select the optimal notification method by referring to the user's past budget overrun history at the time of alert notification. For example, if the user has frequently exceeded the budget in the past, the notification unit immediately notifies the user of an alert. The notification unit can also prioritize and notify specific categories of expenses from the user's past budget overrun history. Furthermore, the notification unit can analyze the user's past budget overrun history and prioritize and notify the most important information. In this way, the optimal notification method can be selected by referring to the user's past budget overrun history. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the user's budget overrun history data into AI, which can select the optimal notification method.

[0064] The notification unit can apply different notification formats depending on the cause of the budget overrun at the time of the alert notification. The notification unit applies different notification formats depending on the cause of the budget overrun at the time of the alert notification. For example, in the case of a food budget overrun, the notification unit provides a notification format that emphasizes ingredients and expiration dates. In addition, in the case of a clothing budget overrun, the notification unit can also provide a notification format that emphasizes design and materials. Furthermore, in the case of an electronic device budget overrun, the notification unit can also provide a notification format that emphasizes technical specifications. In this way, by applying different notification formats depending on the cause of the budget overrun, more appropriate notifications can be provided. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input data on the cause of the budget overrun into AI, and the AI ​​can apply different notification formats.

[0065] The notification unit can select the optimal notification method in consideration of the device information of the user at the time of alert notification. The notification unit selects the optimal notification method in consideration of the device information of the user at the time of alert notification. For example, when the user is using a smartphone, the notification unit provides a notification method that matches the screen size. In addition, when the user is using a tablet, the notification unit can also provide a notification method that is optimized for a large screen. Furthermore, when the user is using a smartwatch, the notification unit can also provide a notification method that is simple and highly visible. This makes it possible to select the optimal notification method by considering the device information of the user. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit inputs the device information of the user into AI, and the AI ​​can select the optimal notification method.

[0066] The notification unit can give priority to highly relevant information in consideration of the geographical location information of the user when notifying the alert. The notification unit gives priority to highly relevant information in consideration of the geographical location information of the user when notifying the alert. For example, the notification unit gives priority to alerts of nearby stores based on the user's current location. The notification unit can also give priority to alerts of products that can be delivered in consideration of the geographical location information of the user. Furthermore, the notification unit can also give priority to area-limited discount information based on the geographical location information of the user. In this way, by taking into consideration the geographical location information of the user, highly relevant information can be given priority. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the geographical location information of the user to AI, and the AI ​​can give priority to highly relevant information.

[0067] The suggestion unit can adjust the level of detail of the proposal based on the importance of points and coupons when making a proposal. The suggestion unit adjusts the level of detail of the proposal based on the importance of points and coupons when making a proposal. For example, the suggestion unit makes a detailed proposal for an expensive product. The suggestion unit can also make a simplified proposal for a low-priced product. Furthermore, the suggestion unit can also make a detailed proposal for a product in which the user is particularly interested. As a result, by adjusting the level of detail of the proposal based on the importance of points and coupons, a more appropriate proposal can be provided. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the importance data of points and coupons to AI, and the AI ​​can adjust the level of detail of the proposal.

[0068] The suggestion unit can apply different suggestion algorithms depending on the category of points or coupons when making a suggestion. The suggestion unit applies different suggestion algorithms depending on the category of points or coupons when making a suggestion. For example, in the case of electronic devices, the suggestion unit makes a suggestion that emphasizes technical specifications. In addition, in the case of clothing, the suggestion unit can also make a suggestion that emphasizes design and materials. Furthermore, in the case of food, the suggestion unit can also make a suggestion that emphasizes ingredients and expiration dates. In this way, by applying different suggestion algorithms depending on the category of points or coupons, more appropriate suggestions can be provided. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input category data of points or coupons to AI, and the AI ​​can apply different suggestion algorithms.

[0069] The suggestion unit can determine the priority of the suggestion based on the expiration date of the points or coupons at the time of suggestion. The suggestion unit determines the priority of the suggestion based on the expiration date of the points or coupons at the time of suggestion. For example, the suggestion unit prioritizes the suggestion when the expiration date of the points is approaching. The suggestion unit can also prioritize the suggestion when the expiration date of the coupon is approaching. Furthermore, the suggestion unit can also prioritize the suggestion of points or coupons that the user is considering using within a specific deadline. In this way, by determining the priority of the suggestion based on the expiration date of the points or coupons, more appropriate suggestions can be provided. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the expiration date data of the points or coupons to AI, and the AI ​​can determine the priority of the suggestion.

[0070] The suggestion unit can adjust the order of suggestions based on the relevance of points and coupons when making suggestions. The suggestion unit adjusts the order of suggestions based on the relevance of points and coupons when making suggestions. For example, when a user has multiple points or coupons, the suggestion unit prioritizes suggestions that are highly relevant. The suggestion unit can also prioritize suggestions of points and coupons in the same category. Furthermore, the suggestion unit can also prioritize suggestions that are highly relevant to points and coupons that the user has used in the past. This allows more appropriate suggestions to be provided by adjusting the order of suggestions based on the relevance of points and coupons. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input relevance data of points and coupons to AI, and the AI ​​can adjust the order of suggestions.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The collection unit can analyze the user's purchase history and identify trends of products purchased in the past. For example, the collection unit can analyze data on products purchased in the past by the user and identify seasonal trends. In addition, if the user shows a high interest in a particular brand or category, the collection unit can also prioritize collecting that trend. Furthermore, the collection unit can identify trends during a particular event or sale period from the user's purchase history and prioritize collecting that information. This allows trends to be identified based on the user's purchase history and more appropriate information to be collected.

[0073] The analysis unit can analyze the user's purchasing patterns and predict future purchasing behavior. For example, the analysis unit can predict the next product that the user is likely to purchase based on data on products the user has purchased in the past. The analysis unit can also analyze the user's purchasing frequency and purchase timing to predict the timing of the next purchase. Furthermore, the analysis unit can predict purchasing behavior during a specific event or sale period from the user's purchasing patterns and provide that information. This makes it possible to predict future purchasing behavior based on the user's purchasing patterns and provide more appropriate information.

[0074] The acquisition unit can access the user's email account and acquire information not only from purchase confirmation emails but also from newsletters and promotional emails. For example, the acquisition unit acquires the latest promotional information from newsletters to which the user subscribes. The acquisition unit can also acquire discount information from promotional emails received by the user. Furthermore, the acquisition unit can acquire information on new products that have arrived in the user's email account and provide that information to the analysis unit. This makes it possible to acquire a variety of information from the user's email account and provide a wider range of information.

[0075] The display unit can adjust the display method based on the usage status of the user's device. For example, when the user is using a smartphone, the display unit provides a display method that matches the screen size. In addition, when the user is using a tablet, the display unit can also provide a display method optimized for a large screen. Furthermore, when the user is using a desktop, the display unit can also provide a display method that includes detailed information. This allows the display method to be adjusted based on the usage status of the user's device to provide a more appropriate display method.

[0076] The notification unit can adjust the timing of the alert taking into account the user's schedule. For example, the notification unit can cooperate with the user's calendar app and delay the alert when there is an important meeting or appointment. The notification unit can also send alerts during times when the user is relaxing. Furthermore, the notification unit can refrain from sending alerts during times when the user is busy and send all the alerts at once later. This allows the timing of the alert to be adjusted based on the user's schedule, making it possible to send notifications at more appropriate times.

[0077] The process flow of the first embodiment will be briefly described below.

[0078] Step 1: The collection unit collects price or discount information from various sites, for example, product information from multiple shopping sites on the Internet. Step 2: The analysis unit analyzes the information collected by the collection unit, and identifies the lowest price by comparing prices and discount information. For example, based on the collected product price information, the lowest price is identified, and the discount information is analyzed to identify the most advantageous discount. Step 3: The comparison table display unit displays the comparison table generated by the analysis unit to the user. For example, the comparison table is displayed in a table format or a graph format. Step 4: The acquisition unit acquires information from the email sent when the product is purchased. For example, the acquisition unit accesses the user's email account and acquires product information from the purchase confirmation email, etc. Step 5: The display unit visually displays the monthly expenses by categorizing them based on the information acquired by the acquisition unit. For example, the expenses may be displayed in a pie chart or a bar graph, allowing the user to grasp the status of expenses at a glance. Step 6: The notification section monitors the budget limit set by the user and notifies the user by alert when the expenditure exceeds the limit. For example, a pop-up notification or an email notification is sent when the budget limit is exceeded. Step 7: The suggestion unit manages the expiration dates of points and coupons held by the user and suggests optimal ways to use them. For example, when the expiration date of points is approaching, the suggestion unit suggests optimal products for using points or stores and products for which coupons can be used.

[0079] (Example 2) The online shopping support system according to the embodiment of the present invention is a system that collects price and discount information from various sites, automatically generates a comparison table using AI, and tells the lowest price. This system acquires information from emails when purchasing products, and visually displays monthly expenses in categories. In addition, the system monitors the budget upper limit set by the user, and notifies the user by alert when the upper limit is exceeded. In addition, the system also manages the expiration dates of points and coupons and suggests how to use them. For example, the collection unit collects product information from multiple shopping sites on the Internet. The collected information is analyzed by AI, and a comparison table is generated. The analysis unit analyzes the collected information, compares prices and discount information, and identifies the lowest price. The generated comparison table is displayed to the user by the comparison table display unit. This allows the user to easily find the lowest-priced product. Next, the acquisition unit accesses the user's email account and acquires product information from purchase confirmation emails, etc. The acquired information is analyzed by AI, and monthly expenses are categorized. The categorized expenses are visually displayed. The display unit displays expenses by category in graphs, charts, etc., allowing the user to grasp the situation of expenses at a glance. Furthermore, the notification unit monitors the budget upper limit set by the user, and issues an alert when the expenditure exceeds the upper limit. This allows the user to manage so as not to exceed the budget. Finally, the suggestion unit manages the expiration date of points and coupons held by the user, and suggests the optimal way to use them. For example, when the expiration date of points is approaching, the suggestion unit suggests the optimal product for using points, or suggests stores and products where coupons can be used. This allows the user to make the most of points and coupons without waste. This allows the online shopping support system to find the lowest price, manage expenditures, and make effective use of points and coupons when a user purchases products online.

[0080] The online shopping support system according to the embodiment includes a collection unit, an analysis unit, a comparison table display unit, an acquisition unit, a display unit, a notification unit, and a suggestion unit. The collection unit collects price or discount information from various sites. The collection unit collects product information from, for example, a plurality of shopping sites on the Internet. The collected information is analyzed by AI, and a comparison table is generated. The analysis unit analyzes the collected information and compares the price and discount information to identify the lowest price. For example, the analysis unit identifies the cheapest price based on the collected price information of the product. The analysis unit can also analyze the discount information and identify the most advantageous discount. The generated comparison table is displayed to the user by the comparison table display unit. For example, the comparison table display unit displays the generated comparison table in a table format. The comparison table display unit can also display in a graph format. The acquisition unit acquires information from an email at the time of purchasing the product. For example, the acquisition unit accesses the user's email account and acquires product information from a purchase confirmation email or the like. The acquired information is analyzed by AI, and monthly expenses are categorized. The display unit displays the expenses by category in a graph or chart, so that the user can grasp the situation of the expenses at a glance. For example, the display unit displays the expenses in a pie chart. The display unit can also display the expenses in a bar graph. The notification unit monitors the budget upper limit set by the user, and notifies the user by an alert when the expenses exceed the upper limit. For example, the notification unit issues a pop-up notification when the budget upper limit is exceeded. The notification unit can also issue an email notification. The suggestion unit manages the expiration date of points and coupons held by the user, and suggests an optimal way to use the points. For example, the suggestion unit suggests an optimal product for using the points when the expiration date of the points is approaching. The suggestion unit can also suggest stores and products for which coupons can be used. As a result, the online shopping support system according to the embodiment can find the lowest price, manage expenses, and effectively use points and coupons when purchasing products online by the user.

[0081] The collection unit collects price or discount information from various sites. The collection unit collects product information from, for example, multiple shopping sites on the Internet. Specifically, the collection unit uses web scraping technology to analyze the HTML structure of each shopping site and extract data such as product name, price, discount information, and stock status. This allows the collection unit to obtain the latest price information and discount information in real time. The collection unit can also use an API to obtain data directly from the shopping site. For example, the API provided by a specific shopping site is used to periodically obtain product information and store it in a database. Furthermore, the collection unit can preferentially collect information on specific products or categories specified by the user. This allows the user to quickly grasp price fluctuations and discount information for products in which the user is interested. The collected information is stored in a database and is ready to be analyzed by the analysis unit.

[0082] The analysis unit analyzes the collected information and compares prices and discount information to identify the lowest price. Specifically, the analysis unit uses an AI algorithm to analyze the collected data and compares the price and discount information of each product. For example, the AI ​​uses a machine learning model to analyze the price history of each product and identify price trends and fluctuation patterns. In addition, the AI ​​uses natural language processing technology to analyze the text of the discount information and extract the discount rate and application conditions. This allows the analysis unit to identify the cheapest price and the most advantageous discount. Furthermore, the analysis unit can also recommend the most suitable product individually, taking into account the user's purchase history and preferences. For example, based on the trends of products purchased in the past, the analysis unit identifies products that the user is likely to be interested in and provides the lowest price and discount information for those products. Based on this information, the analysis unit presents the most advantageous purchase options for the user.

[0083] The generated comparison table is displayed to the user by the comparison table display unit. Specifically, the comparison table display unit displays the generated comparison table in a table format. For example, information such as the price, discount rate, sales site, and stock status of each product is displayed in a table format, allowing the user to easily compare. The comparison table display unit can also display in a graph format. For example, price fluctuations are displayed in a line graph, allowing the user to visually check past price history. Furthermore, the comparison table display unit provides a filter function that can be customized by the user, allowing the user to narrow down products based on specific conditions. For example, the user can narrow down the products displayed by specifying conditions such as a price range, discount rate, and sales site. This allows the user to efficiently find products that meet his or her needs.

[0084] The acquisition unit acquires information from the email sent when the product is purchased. Specifically, the acquisition unit accesses the user's email account and acquires product information from the purchase confirmation email, etc. For example, the acquisition unit connects to the user's email server using IMAP or POP3 protocol and searches for the purchase confirmation email. The purchase confirmation email contains information such as the product name, purchase price, purchase date, and sales site, and the acquisition unit extracts this information and stores it in a database. Furthermore, the acquisition unit uses AI to analyze the contents of the email and automatically extracts important information. For example, natural language processing technology is used to identify the product name and price from the email body and register them in a database. As a result, the acquisition unit accurately grasps the user's purchase history and makes it available to the analysis unit and display unit.

[0085] The display unit displays the expenses for each category in a graph or chart, so that the user can grasp the situation of the expenses at a glance. Specifically, the display unit displays the expenses for each category in a pie chart or a bar graph based on the collected purchase information. For example, the expenses are totaled for each category such as food, clothing, and home appliances, and the percentage is displayed in a pie chart. In addition, the monthly expenses are displayed in a bar graph, so that the transition of expenses can be visually confirmed. Furthermore, the display unit provides a filter function that can be customized by the user, and can display expenses narrowed down to a specific period or category. For example, by displaying expenses for the past three months and expenses for a specific category, the user can grasp the detailed situation of the expenses. In this way, the display unit supports the user in efficiently managing expenses.

[0086] The notification unit monitors the budget upper limit set by the user, and notifies the user by an alert when the expenditure exceeds the upper limit. Specifically, the notification unit saves the budget upper limit set by the user in a database and monitors expenditure information in real time. For example, the user sets a monthly budget upper limit, and issues an alert when the expenditure approaches the upper limit. The notification unit sends the alert to the user using multiple notification means, such as a pop-up notification, an email notification, or a push notification on a smartphone. Furthermore, the notification unit can also suggest specific saving methods or alternatives when the expenditure exceeds the budget upper limit. For example, the notification unit supports the user to continue shopping within the budget by providing money saving techniques for suppressing expenditure and discount information. In this way, the notification unit prevents the user from spending more than the budget and enables the user to make planned shopping.

[0087] The suggestion unit manages the expiration dates of points and coupons held by the user and suggests optimal ways to use them. Specifically, the suggestion unit stores information on points and coupons held by the user in a database and monitors the expiration dates. For example, when the expiration date of points is approaching, the suggestion unit suggests optimal products and services for using points. It also identifies stores and products for which coupons can be used and notifies the user. Furthermore, the suggestion unit uses AI to analyze the user's purchase history and preferences and makes optimal suggestions individually. For example, it identifies products that the user may be interested in based on past purchase history and suggests coupons that can be applied to those products. In this way, the suggestion unit supports the user to make effective use of points and coupons and enjoy shopping at a better price.

[0088] The collection unit can collect product information from multiple shopping sites on the Internet. The collection unit, for example, collects product information from multiple shopping sites on the Internet. For example, the collection unit acquires information from an e-commerce site, a price comparison site, and the like. The collected information is analyzed by AI, and a comparison table is generated. In this way, a wide range of information can be obtained by collecting product information from multiple shopping sites. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can collect product information from multiple shopping sites on the Internet, input the information to AI, and the AI ​​can perform analysis.

[0089] The analysis unit can analyze the collected information and compare prices and discount information to identify the lowest price. The analysis unit can analyze the collected information and compare prices and discount information to identify the lowest price. For example, the analysis unit can identify the lowest price based on the collected price information of the product. The analysis unit can also analyze the discount information to identify the most advantageous discount. For example, the analysis unit inputs the price information of the product into an AI, which identifies the lowest price. This allows the user to find the most advantageous product by comparing the price and discount information to identify the lowest price. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the analysis unit can input the collected information into an AI, which analyzes the price and discount information to identify the lowest price.

[0090] The acquisition unit can access the user's email account and acquire product information from a purchase confirmation email or the like. The acquisition unit can access the user's email account and acquire product information from a purchase confirmation email or the like. For example, the acquisition unit can access the user's email account and acquire product information from a purchase confirmation email, a shipping notification email, or the like. The acquired information is analyzed by AI, and monthly expenses are categorized. In this way, by acquiring product information from the user's email account, the purchase history can be automatically managed. Some or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can access the user's email account, input the acquired information into AI, and the AI ​​can perform analysis.

[0091] The display unit can display the expenses by category in a graph or chart, allowing the user to grasp the status of the expenses at a glance. The display unit can display the expenses by category in a graph or chart, allowing the user to grasp the status of the expenses at a glance. For example, the display unit displays the expenses in a pie chart. The display unit can also display the expenses in a bar graph. This allows the user to easily manage the expenses by visually displaying the status of the expenses. Some or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input the expense data to AI, which can generate graphs and charts.

[0092] The notification unit can monitor the budget upper limit set by the user and notify by an alert when the expenditure exceeds the upper limit. The notification unit can monitor the budget upper limit set by the user and notify by an alert when the expenditure exceeds the upper limit. For example, the notification unit issues a pop-up notification when the budget upper limit is exceeded. The notification unit can also issue an email notification. This allows the user to manage the budget so that it does not exceed the budget by notifying by an alert when the budget upper limit is exceeded. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI or may be performed without using AI. For example, the notification unit can input expenditure data into AI, which monitors the budget upper limit and notifies by an alert when it is exceeded.

[0093] The suggestion unit can manage the expiration dates of points and coupons held by the user and suggest appropriate ways to use them. The suggestion unit can manage the expiration dates of points and coupons held by the user and suggest appropriate ways to use them. For example, when the expiration date of points is approaching, the suggestion unit can suggest the optimal product for using points. The suggestion unit can also suggest stores and products where coupons can be used. In this way, by managing the expiration dates of points and coupons and suggesting the optimal ways to use them, the user can use points and coupons without waste. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data of points and coupons into AI, which can suggest the optimal ways to use them.

[0094] The collection unit can estimate the user's emotion and determine the priority of the sites to be collected based on the estimated user's emotion. The collection unit estimates the user's emotion and determines the priority of the sites to be collected based on the estimated user's emotion. For example, when the user is stressed, the collection unit prioritizes and collects highly reliable sites. In addition, when the user is relaxed, the collection unit can also collect information from a wide range of sites. Furthermore, when the user is in a hurry, the collection unit can also prioritize and collect sites that can provide immediate results. In this way, more appropriate information can be collected by determining the priority of the sites to be collected based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI, which can estimate the emotion, and determine the priority of the sites to be collected based on the result.

[0095] The collection unit can analyze the user's past purchase history and select the optimal collection method. The collection unit analyzes the user's past purchase history and selects the optimal collection method. For example, the collection unit preferentially collects information from sites that the user has frequently used in the past. The collection unit can also preferentially collect product information of a specific category from the user's past purchase history. Furthermore, the collection unit can also analyze the user's past purchase history and collect information from the most reliable site. In this way, the optimal collection method can be selected by analyzing the user's past purchase history. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's purchase history data into AI, which can select the optimal collection method.

[0096] The collection unit can perform filtering based on the user's current purchasing willingness at the time of collection. The collection unit performs filtering based on the user's current purchasing willingness at the time of collection. For example, when the user shows a high purchasing willingness, the collection unit prioritizes collecting the latest discount information. In addition, when the user shows a low purchasing willingness, the collection unit can also collect general price information. Furthermore, the collection unit can filter and collect product information of a specific brand or category according to the user's purchasing willingness. This makes it possible to collect more appropriate information by filtering information based on the user's purchasing willingness. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's purchasing willingness data into AI, and the AI ​​can perform filtering.

[0097] The collection unit can estimate the user's emotion and determine the priority order of products to be collected based on the estimated user's emotion. The collection unit estimates the user's emotion and determines the priority order of products to be collected based on the estimated user's emotion. For example, when the user is excited, the collection unit prioritizes collecting popular products. In addition, when the user is relaxed, the collection unit can also collect information on a wide range of products. Furthermore, when the user is in a hurry, the collection unit can also prioritize collecting information on products that can be purchased immediately. In this way, by determining the priority order of products to be collected based on the user's emotion, more appropriate information can be collected. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to the generation AI, which can estimate the emotion, and determine the priority order of products to be collected based on the result.

[0098] The collection unit can preferentially collect highly relevant information in consideration of the geographical location information of the user at the time of collection. The collection unit preferentially collects highly relevant information in consideration of the geographical location information of the user at the time of collection. For example, the collection unit preferentially collects information of nearby stores based on the user's current location. The collection unit can also preferentially collect information of deliverable products in consideration of the geographical location information of the user. Furthermore, the collection unit can also preferentially collect area-limited discount information based on the geographical location information of the user. In this way, highly relevant information can be preferentially collected by considering the geographical location information of the user. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to AI, and the AI ​​can preferentially collect highly relevant information.

[0099] The collection unit can analyze the user's social media activity at the time of collection and collect related information. The collection unit can analyze the user's social media activity at the time of collection and collect related information. For example, the collection unit preferentially collects product information in which the user has shown interest on social media. The collection unit can also collect related discount information from the user's social media activity. Furthermore, the collection unit can also collect product information purchased by the user's followers and friends on social media. In this way, related information can be collected by analyzing the user's social media activity. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the user's social media data into AI, and the AI ​​can collect related information.

[0100] The analysis unit can estimate the user's emotion and adjust the accuracy of the analysis based on the estimated user's emotion. The analysis unit can estimate the user's emotion and adjust the accuracy of the analysis based on the estimated user's emotion. For example, the analysis unit performs a detailed analysis when the user is relaxed. The analysis unit can also perform a quick analysis when the user is in a hurry. Furthermore, the analysis unit can provide a visually easy-to-understand analysis result when the user is excited. As a result, a more appropriate analysis result can be obtained by adjusting the accuracy of the analysis based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI, which estimates the emotion, and adjust the accuracy of the analysis based on the result.

[0101] The analysis unit can adjust the level of detail of the analysis based on the importance of the product during the analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the product during the analysis. For example, the analysis unit performs a detailed analysis for an expensive product. The analysis unit can also perform a simplified analysis for a low-priced product. Furthermore, the analysis unit can perform a detailed analysis for a product in which the user is particularly interested. As a result, by adjusting the level of detail of the analysis based on the importance of the product, a more appropriate analysis result can be obtained. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the importance data of the product to an AI, which can adjust the level of detail of the analysis.

[0102] The analysis unit can apply different analysis algorithms depending on the category of the product during analysis. The analysis unit applies different analysis algorithms depending on the category of the product during analysis. For example, in the case of electronic devices, the analysis unit performs analysis that emphasizes technical specifications. In addition, in the case of clothing, the analysis unit can also perform analysis that emphasizes design and materials. Furthermore, in the case of food, the analysis unit can also perform analysis that emphasizes ingredients and expiration dates. In this way, by applying different analysis algorithms depending on the category of the product, more appropriate analysis results can be obtained. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input product category data to AI, and the AI ​​can apply different analysis algorithms.

[0103] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. The analysis unit estimates the user's emotion and adjusts the display method of the analysis result based on the estimated user's emotion. For example, the analysis unit provides a simple and highly visible display method when the user is nervous. The analysis unit can also provide a display method including detailed information when the user is relaxed. Furthermore, the analysis unit can also provide a display method that focuses on the main points when the user is in a hurry. This makes it possible to provide a more appropriate display method by adjusting the display method of the analysis result based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI, which can estimate the emotion, and adjust the display method of the analysis result based on the result.

[0104] The analysis unit can determine the priority of the analysis based on the submission time of the product during the analysis. The analysis unit determines the priority of the analysis based on the submission time of the product during the analysis. For example, the analysis unit performs analysis preferentially when a new product is released. The analysis unit can also prioritize the analysis of products during a sale period. Furthermore, the analysis unit can prioritize the analysis of products that the user is considering purchasing within a specific deadline. In this way, by determining the priority of the analysis based on the submission time of the product, more appropriate analysis results can be obtained. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the submission time data of the product to AI, and the AI ​​can determine the priority of the analysis.

[0105] The analysis unit can adjust the order of analysis based on the relevance of the products during analysis. The analysis unit adjusts the order of analysis based on the relevance of the products during analysis. For example, when the user is comparing multiple products, the analysis unit prioritizes the analysis of highly related products. The analysis unit can also prioritize the analysis of products in the same category. Furthermore, the analysis unit can also prioritize the analysis of products that are highly related to products purchased by the user in the past. This makes it possible to obtain more appropriate analysis results by adjusting the order of analysis based on the relevance of the products. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input product relevance data to AI, and the AI ​​can adjust the order of analysis.

[0106] The comparison table display unit can estimate the user's emotion and adjust the display method of the comparison table based on the estimated user's emotion. The comparison table display unit estimates the user's emotion and adjusts the display method of the comparison table based on the estimated user's emotion. For example, the comparison table display unit provides a simple and highly visible display method when the user is nervous. In addition, the comparison table display unit can also provide a display method including detailed information when the user is relaxed. Furthermore, the comparison table display unit can also provide a display method that focuses on the main points when the user is in a hurry. This allows a more appropriate display method to be provided by adjusting the display method of the comparison table based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the comparison table display unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the comparison table display unit can input a user's emotional data into the generation AI, which can then infer the emotion and adjust the way the comparison table is displayed based on the inference.

[0107] The comparison table display unit can select the optimal display method by referring to the user's past browsing history when displaying the comparison table. The comparison table display unit can select the optimal display method by referring to the user's past browsing history when displaying the comparison table. For example, the comparison table display unit preferentially displays related information based on product information previously viewed by the user. The comparison table display unit can also preferentially display product information of a specific category from the user's past browsing history. Furthermore, the comparison table display unit can analyze the user's past browsing history and preferentially display information of the greatest interest. This allows the optimal display method to be selected by referring to the user's past browsing history. A part or all of the above-mentioned processing in the comparison table display unit may be performed, for example, using AI, or may be performed without using AI. For example, the comparison table display unit can input the user's browsing history data into AI, which can select the optimal display method.

[0108] The comparison table display unit can apply different display formats for each product category when displaying the comparison table. The comparison table display unit can apply different display formats for each product category when displaying the comparison table. For example, in the case of electronic devices, the comparison table display unit provides a display format that emphasizes technical specifications. In addition, in the case of clothing, the comparison table display unit can also provide a display format that emphasizes design and materials. Furthermore, in the case of food, the comparison table display unit can also provide a display format that emphasizes ingredients and expiration dates. This allows a more appropriate display method to be provided by applying different display formats for each product category. Part or all of the above-mentioned processing in the comparison table display unit may be performed, for example, using AI, or may be performed without using AI. For example, the comparison table display unit can input product category data into AI, and the AI ​​can apply different display formats.

[0109] The comparison table display unit can estimate the user's emotion and adjust the display order of the comparison table based on the estimated user's emotion. The comparison table display unit estimates the user's emotion and adjusts the display order of the comparison table based on the estimated user's emotion. For example, when the user is excited, the comparison table display unit preferentially displays popular products. In addition, when the user is relaxed, the comparison table display unit can also display information on a wide range of products. Furthermore, when the user is in a hurry, the comparison table display unit can also preferentially display information on products that can be purchased immediately. This allows a more appropriate display order to be provided by adjusting the display order of the comparison table based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the comparison table display unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the comparison table display unit can input the user's emotion data to the generation AI, which estimates the emotion, and adjust the display order of the comparison table based on the result.

[0110] The comparison table display unit can select the optimal display method in consideration of the device information of the user when displaying the comparison table. The comparison table display unit selects the optimal display method in consideration of the device information of the user when displaying the comparison table. For example, when the user is using a smartphone, the comparison table display unit provides a display method that matches the screen size. In addition, when the user is using a tablet, the comparison table display unit can also provide a display method optimized for a large screen. Furthermore, when the user is using a smart watch, the comparison table display unit can also provide a display method that is simple and highly visible. This makes it possible to select the optimal display method by considering the device information of the user. Part or all of the above-mentioned processing in the comparison table display unit may be performed, for example, using AI, or may be performed without using AI. For example, the comparison table display unit can input the device information of the user into AI, and the AI ​​can select the optimal display method.

[0111] The comparison table display unit can display highly relevant information preferentially in consideration of the user's geographical location information when displaying the comparison table. The comparison table display unit displays highly relevant information preferentially in consideration of the user's geographical location information when displaying the comparison table. For example, the comparison table display unit preferentially displays information of nearby stores based on the user's current location. The comparison table display unit can also preferentially display information of products that can be delivered in consideration of the user's geographical location information. Furthermore, the comparison table display unit can also preferentially display area-limited discount information based on the user's geographical location information. This makes it possible to preferentially display highly relevant information by considering the user's geographical location information. A part or all of the above-mentioned processing in the comparison table display unit may be performed using, for example, AI, or may be performed without using AI. For example, the comparison table display unit can input the user's geographical location information into AI, and the AI ​​can display highly relevant information preferentially.

[0112] The acquisition unit can estimate the user's emotion and adjust the timing of acquiring information from the email based on the estimated user's emotion. The acquisition unit estimates the user's emotion and adjusts the timing of acquiring information from the email based on the estimated user's emotion. For example, the acquisition unit immediately acquires information from the email when the user is relaxed. Also, the acquisition unit can schedule the acquisition of information later when the user is busy. Furthermore, the acquisition unit can prioritize acquiring important information when the user is excited. In this way, by adjusting the timing of acquiring information from the email based on the user's emotion, information can be acquired at a more appropriate timing. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's emotion data to the generation AI, which can estimate the emotion, and adjust the timing of acquiring information based on the result.

[0113] The acquisition unit can analyze the user's past email history and select the optimal acquisition method. The acquisition unit analyzes the user's past email history and selects the optimal acquisition method. For example, the acquisition unit preferentially acquires information from emails that the user frequently received in the past. The acquisition unit can also preferentially acquire information of a specific category from the user's past email history. Furthermore, the acquisition unit can analyze the user's past email history and preferentially acquire the most important information. In this way, the optimal acquisition method can be selected by analyzing the user's past email history. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit inputs the user's email history data into AI, and the AI ​​can select the optimal acquisition method.

[0114] The acquisition unit can perform filtering based on the user's current purchasing willingness when acquiring information from the email. The acquisition unit performs filtering based on the user's current purchasing willingness when acquiring information from the email. For example, when the user shows a high purchasing willingness, the acquisition unit prioritizes acquiring information from the latest purchase confirmation email. In addition, when the user shows a low purchasing willingness, the acquisition unit can also acquire general information. Furthermore, the acquisition unit can filter and acquire information on a specific brand or category according to the user's purchasing willingness. This makes it possible to acquire more appropriate information by filtering information based on the user's purchasing willingness. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input the user's purchasing willingness data into AI, and the AI ​​can perform filtering.

[0115] The acquisition unit can estimate the user's emotion and determine the priority of the emails to be acquired based on the estimated user's emotion. The acquisition unit estimates the user's emotion and determines the priority of the emails to be acquired based on the estimated user's emotion. For example, when the user is excited, the acquisition unit prioritizes acquiring important emails. In addition, when the user is relaxed, the acquisition unit can also acquire information from a wide range of emails. Furthermore, when the user is in a hurry, the acquisition unit can also prioritize acquiring emails that require immediate response. In this way, by determining the priority of the emails to be acquired based on the user's emotion, more appropriate information can be acquired. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's emotion data to the generation AI, which can estimate the emotion, and determine the priority of the emails based on the result.

[0116] The acquisition unit can preferentially acquire highly relevant information in consideration of the geographical location information of the user when acquiring information from the email. The acquisition unit preferentially acquires highly relevant information in consideration of the geographical location information of the user when acquiring information from the email. For example, the acquisition unit preferentially acquires emails from nearby stores based on the user's current location. The acquisition unit can also preferentially acquire information on deliverable products in consideration of the geographical location information of the user. Furthermore, the acquisition unit can also preferentially acquire area-limited discount information based on the geographical location information of the user. In this way, highly relevant information can be preferentially acquired by considering the geographical location information of the user. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input the geographical location information of the user to AI, and the AI ​​can preferentially acquire highly relevant information.

[0117] The acquisition unit can analyze the user's social media activity and acquire related information when acquiring information from the email. The acquisition unit can analyze the user's social media activity and acquire related information when acquiring information from the email. For example, the acquisition unit preferentially acquires product information in which the user has shown interest on social media. The acquisition unit can also acquire related discount information from the user's social media activity. Furthermore, the acquisition unit can also acquire product information purchased by the user's followers and friends on social media. In this way, related information can be acquired by analyzing the user's social media activity. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input the user's social media data into AI, and the AI ​​can acquire related information.

[0118] The display unit can estimate the user's emotion and adjust the display method of the expenses based on the estimated user's emotion. The display unit estimates the user's emotion and adjusts the display method of the expenses based on the estimated user's emotion. For example, the display unit provides a simple and highly visible display method when the user is nervous. The display unit can also provide a display method including detailed information when the user is relaxed. Furthermore, the display unit can also provide a display method that focuses on the main points when the user is in a hurry. This allows a more appropriate display method to be provided by adjusting the display method of the expenses based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the display unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the display unit can input the user's emotion data to the generation AI, which estimates the emotion, and adjust the display method of the expenses based on the result.

[0119] The display unit can select the optimal display method by referring to the user's past expenditure history when displaying the expenditure. The display unit selects the optimal display method by referring to the user's past expenditure history when displaying the expenditure. For example, the display unit preferentially displays categories in which the user has frequently spent in the past. The display unit can also preferentially display expenditures for a specific period from the user's past expenditure history. Furthermore, the display unit can analyze the user's past expenditure history and preferentially display information of the greatest interest. This allows the optimal display method to be selected by referring to the user's past expenditure history. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit inputs the user's expenditure history data into AI, which can select the optimal display method.

[0120] The display unit can apply different display formats for each category when displaying expenses. The display unit can apply different display formats for each category when displaying expenses. For example, in the case of food, the display unit provides a display format that emphasizes ingredients and expiration dates. In addition, in the case of clothing, the display unit can also provide a display format that emphasizes design and materials. Furthermore, in the case of electronic devices, the display unit can also provide a display format that emphasizes technical specifications. In this way, by applying different display formats for each category, a more appropriate display method can be provided. Part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input category data to AI, and the AI ​​can apply different display formats.

[0121] The display unit can estimate the user's emotion and adjust the display order of the expenses based on the estimated user's emotion. The display unit estimates the user's emotion and adjusts the display order of the expenses based on the estimated user's emotion. For example, when the user is excited, the display unit prioritizes and displays important expenses. In addition, the display unit can also display a wide range of expense information when the user is relaxed. Furthermore, when the user is in a hurry, the display unit can prioritize and display expense information that requires immediate response. This allows a more appropriate display order to be provided by adjusting the display order of expenses based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the display unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the display unit can input the user's emotion data to the generation AI, which estimates the emotion, and adjust the display order of the expenses based on the result.

[0122] The display unit can select the optimal display method in consideration of the user's device information when displaying expenses. The display unit selects the optimal display method in consideration of the user's device information when displaying expenses. For example, when the user is using a smartphone, the display unit provides a display method that matches the screen size. In addition, when the user is using a tablet, the display unit can also provide a display method optimized for a large screen. Furthermore, when the user is using a smart watch, the display unit can also provide a simple and highly visible display method. This allows the optimal display method to be selected by considering the user's device information. Part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit inputs the user's device information into AI, and the AI ​​can select the optimal display method.

[0123] The display unit can display highly relevant information preferentially in consideration of the geographical location information of the user when displaying expenses. The display unit displays highly relevant information preferentially in consideration of the geographical location information of the user when displaying expenses. For example, the display unit preferentially displays expense information of nearby stores based on the user's current location. The display unit can also preferentially display expense information of deliverable products in consideration of the geographical location information of the user. Furthermore, the display unit can also preferentially display area-limited discount information based on the geographical location information of the user. This makes it possible to preferentially display highly relevant information by considering the geographical location information of the user. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input the geographical location information of the user to AI, and the AI ​​can display highly relevant information preferentially.

[0124] The notification unit can estimate the user's emotion and adjust the display method of the alert based on the estimated user's emotion. The notification unit estimates the user's emotion and adjusts the display method of the alert based on the estimated user's emotion. For example, the notification unit displays a simple and highly visible alert when the user is nervous. The notification unit can also display an alert including detailed information when the user is relaxed. Furthermore, the notification unit can display an alert with the main points when the user is in a hurry. This allows a more appropriate display method to be provided by adjusting the display method of the alert based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the user's emotion data to the generation AI, which can estimate the emotion, and adjust the display method of the alert based on the result.

[0125] The notification unit can select the optimal notification method by referring to the user's past budget overrun history at the time of alert notification. The notification unit can select the optimal notification method by referring to the user's past budget overrun history at the time of alert notification. For example, if the user has frequently exceeded the budget in the past, the notification unit immediately notifies the user of an alert. The notification unit can also prioritize and notify specific categories of expenses from the user's past budget overrun history. Furthermore, the notification unit can analyze the user's past budget overrun history and prioritize and notify the most important information. In this way, the optimal notification method can be selected by referring to the user's past budget overrun history. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the user's budget overrun history data into AI, which can select the optimal notification method.

[0126] The notification unit can apply different notification formats depending on the cause of the budget overrun at the time of the alert notification. The notification unit applies different notification formats depending on the cause of the budget overrun at the time of the alert notification. For example, in the case of a food budget overrun, the notification unit provides a notification format that emphasizes ingredients and expiration dates. In addition, in the case of a clothing budget overrun, the notification unit can also provide a notification format that emphasizes design and materials. Furthermore, in the case of an electronic device budget overrun, the notification unit can also provide a notification format that emphasizes technical specifications. In this way, by applying different notification formats depending on the cause of the budget overrun, more appropriate notifications can be provided. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input data on the cause of the budget overrun into AI, and the AI ​​can apply different notification formats.

[0127] The notification unit can estimate the user's emotion and determine the priority of the alert based on the estimated user's emotion. The notification unit can estimate the user's emotion and determine the priority of the alert based on the estimated user's emotion. For example, when the user is excited, the notification unit prioritizes important alerts. In addition, when the user is relaxed, the notification unit can also prioritize a wide range of alerts. Furthermore, when the user is in a hurry, the notification unit can also prioritize alerts that require immediate response. In this way, by determining the priority of the alert based on the user's emotion, more appropriate notifications can be provided. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the user's emotion data to the generation AI, which can estimate the emotion, and determine the priority of the alert based on the result.

[0128] The notification unit can select the optimal notification method in consideration of the device information of the user at the time of alert notification. The notification unit selects the optimal notification method in consideration of the device information of the user at the time of alert notification. For example, when the user is using a smartphone, the notification unit provides a notification method that matches the screen size. In addition, when the user is using a tablet, the notification unit can also provide a notification method that is optimized for a large screen. Furthermore, when the user is using a smartwatch, the notification unit can also provide a notification method that is simple and highly visible. This makes it possible to select the optimal notification method by considering the device information of the user. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit inputs the device information of the user into AI, and the AI ​​can select the optimal notification method.

[0129] The notification unit can give priority to highly relevant information in consideration of the geographical location information of the user when notifying the alert. The notification unit gives priority to highly relevant information in consideration of the geographical location information of the user when notifying the alert. For example, the notification unit gives priority to alerts of nearby stores based on the user's current location. The notification unit can also give priority to alerts of products that can be delivered in consideration of the geographical location information of the user. Furthermore, the notification unit can also give priority to area-limited discount information based on the geographical location information of the user. In this way, by taking into consideration the geographical location information of the user, highly relevant information can be given priority. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the geographical location information of the user to AI, and the AI ​​can give priority to highly relevant information.

[0130] The suggestion unit can estimate the user's emotion and adjust the way of expressing the proposal based on the estimated user's emotion. The suggestion unit can estimate the user's emotion and adjust the way of expressing the proposal based on the estimated user's emotion. For example, the suggestion unit provides a simple and highly visible proposal when the user is nervous. In addition, the suggestion unit can also provide a proposal including detailed information when the user is relaxed. Furthermore, the suggestion unit can also provide a proposal that covers the main points when the user is in a hurry. This allows a more appropriate proposal to be provided by adjusting the way of expressing the proposal based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data to the generation AI, which can estimate the emotion, and adjust the way of expressing the proposal based on the result.

[0131] The suggestion unit can adjust the level of detail of the proposal based on the importance of points and coupons when making a proposal. The suggestion unit adjusts the level of detail of the proposal based on the importance of points and coupons when making a proposal. For example, the suggestion unit makes a detailed proposal for an expensive product. The suggestion unit can also make a simplified proposal for a low-priced product. Furthermore, the suggestion unit can also make a detailed proposal for a product in which the user is particularly interested. As a result, by adjusting the level of detail of the proposal based on the importance of points and coupons, a more appropriate proposal can be provided. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the importance data of points and coupons to AI, and the AI ​​can adjust the level of detail of the proposal.

[0132] The suggestion unit can apply different suggestion algorithms depending on the category of points or coupons when making a suggestion. The suggestion unit applies different suggestion algorithms depending on the category of points or coupons when making a suggestion. For example, in the case of electronic devices, the suggestion unit makes a suggestion that emphasizes technical specifications. In addition, in the case of clothing, the suggestion unit can also make a suggestion that emphasizes design and materials. Furthermore, in the case of food, the suggestion unit can also make a suggestion that emphasizes ingredients and expiration dates. In this way, by applying different suggestion algorithms depending on the category of points or coupons, more appropriate suggestions can be provided. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input category data of points or coupons to AI, and the AI ​​can apply different suggestion algorithms.

[0133] The suggestion unit can estimate the user's emotion and adjust the length of the proposal based on the estimated user's emotion. The suggestion unit can estimate the user's emotion and adjust the length of the proposal based on the estimated user's emotion. For example, the suggestion unit provides a short and to the point proposal when the user is in a hurry. The suggestion unit can also provide a longer proposal including a detailed explanation when the user is relaxed. Furthermore, the suggestion unit can also provide a proposal with a visually stimulating effect when the user is excited. This allows the proposal to be adjusted in length based on the user's emotion, thereby providing a more appropriate proposal. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data to the generation AI, which can estimate the emotion, and adjust the length of the proposal based on the result.

[0134] The suggestion unit can determine the priority of the suggestion based on the expiration date of the points or coupons at the time of suggestion. The suggestion unit determines the priority of the suggestion based on the expiration date of the points or coupons at the time of suggestion. For example, the suggestion unit prioritizes the suggestion when the expiration date of the points is approaching. The suggestion unit can also prioritize the suggestion when the expiration date of the coupon is approaching. Furthermore, the suggestion unit can also prioritize the suggestion of points or coupons that the user is considering using within a specific deadline. In this way, by determining the priority of the suggestion based on the expiration date of the points or coupons, more appropriate suggestions can be provided. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the expiration date data of the points or coupons to AI, and the AI ​​can determine the priority of the suggestion.

[0135] The suggestion unit can adjust the order of suggestions based on the relevance of points and coupons when making suggestions. The suggestion unit adjusts the order of suggestions based on the relevance of points and coupons when making suggestions. For example, when a user has multiple points or coupons, the suggestion unit prioritizes suggestions that are highly relevant. The suggestion unit can also prioritize suggestions of points and coupons in the same category. Furthermore, the suggestion unit can also prioritize suggestions that are highly relevant to points and coupons that the user has used in the past. This allows more appropriate suggestions to be provided by adjusting the order of suggestions based on the relevance of points and coupons. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input relevance data of points and coupons to AI, and the AI ​​can adjust the order of suggestions.

[0136] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0137] The collection unit can analyze the user's purchase history and identify trends of products purchased in the past. For example, the collection unit can analyze data on products purchased in the past by the user and identify seasonal trends. In addition, if the user shows a high interest in a particular brand or category, the collection unit can also prioritize collecting that trend. Furthermore, the collection unit can identify trends during a particular event or sale period from the user's purchase history and prioritize collecting that information. This allows trends to be identified based on the user's purchase history and more appropriate information to be collected.

[0138] The analysis unit can analyze the user's purchasing patterns and predict future purchasing behavior. For example, the analysis unit can predict the next product that the user is likely to purchase based on data on products the user has purchased in the past. The analysis unit can also analyze the user's purchasing frequency and purchase timing to predict the timing of the next purchase. Furthermore, the analysis unit can predict purchasing behavior during a specific event or sale period from the user's purchasing patterns and provide that information. This makes it possible to predict future purchasing behavior based on the user's purchasing patterns and provide more appropriate information.

[0139] The acquisition unit can access the user's email account and acquire information not only from purchase confirmation emails but also from newsletters and promotional emails. For example, the acquisition unit acquires the latest promotional information from newsletters to which the user subscribes. The acquisition unit can also acquire discount information from promotional emails received by the user. Furthermore, the acquisition unit can acquire information on new products that have arrived in the user's email account and provide that information to the analysis unit. This makes it possible to acquire a variety of information from the user's email account and provide a wider range of information.

[0140] The display unit can adjust the display method based on the usage status of the user's device. For example, when the user is using a smartphone, the display unit provides a display method that matches the screen size. In addition, when the user is using a tablet, the display unit can also provide a display method optimized for a large screen. Furthermore, when the user is using a desktop, the display unit can also provide a display method that includes detailed information. This allows the display method to be adjusted based on the usage status of the user's device to provide a more appropriate display method.

[0141] The notification unit can adjust the timing of the alert taking into account the user's schedule. For example, the notification unit can cooperate with the user's calendar app and delay the alert when there is an important meeting or appointment. The notification unit can also send alerts during times when the user is relaxing. Furthermore, the notification unit can refrain from sending alerts during times when the user is busy and send all the alerts at once later. This allows the timing of the alert to be adjusted based on the user's schedule, making it possible to send notifications at more appropriate times.

[0142] The collection unit can estimate the user's emotions and determine the priority order of products to be collected based on the estimated user's emotions. For example, when the user is excited, the collection unit prioritizes collecting popular products. Also, when the user is relaxed, the collection unit can collect information on a wide range of products. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting information on products that can be purchased immediately. In this way, by determining the priority order of products to be collected based on the user's emotions, more appropriate information can be collected.

[0143] The analysis unit can estimate the user's emotion and adjust the accuracy of the analysis based on the estimated user's emotion. For example, the analysis unit performs a detailed analysis when the user is relaxed. Also, the analysis unit can perform a quick analysis when the user is in a hurry. Furthermore, the analysis unit can provide a visually easy-to-understand analysis result when the user is excited. In this way, by adjusting the accuracy of the analysis based on the user's emotion, a more appropriate analysis result can be obtained.

[0144] The comparison table display unit can estimate the user's emotions and adjust the display method of the comparison table based on the estimated user's emotions. For example, when the user is nervous, the comparison table display unit provides a simple and highly visible display method. When the user is relaxed, the comparison table display unit can also provide a display method including detailed information. Furthermore, when the user is in a hurry, the comparison table display unit can also provide a display method that focuses on the main points. In this way, a more appropriate display method can be provided by adjusting the display method of the comparison table based on the user's emotions.

[0145] The acquisition unit can estimate the user's emotion and adjust the timing of acquiring information from the email based on the estimated emotion of the user. For example, when the user is relaxed, the acquisition unit can instantly acquire information from the email. Also, when the user is busy, the acquisition unit can schedule the information acquisition to be performed later. Furthermore, when the user is excited, the acquisition unit can prioritize acquiring important information. In this way, by adjusting the timing of acquiring information from the email based on the user's emotion, it is possible to acquire information at a more appropriate timing.

[0146] The notification unit can estimate the user's emotions and adjust the display method of the alert based on the estimated user's emotions. For example, when the user is nervous, the notification unit displays a simple, highly visible alert. When the user is relaxed, the notification unit can also display an alert including detailed information. Furthermore, when the user is in a hurry, the notification unit can also display an alert that focuses on the main points. In this way, a more appropriate display method can be provided by adjusting the display method of the alert based on the user's emotions.

[0147] The process flow of the second embodiment will be briefly described below.

[0148] Step 1: The collection unit collects price or discount information from various sites, for example, product information from multiple shopping sites on the Internet. Step 2: The analysis unit analyzes the information collected by the collection unit, and identifies the lowest price by comparing prices and discount information. For example, based on the collected product price information, the lowest price is identified, and the discount information is analyzed to identify the most advantageous discount. Step 3: The comparison table display unit displays the comparison table generated by the analysis unit to the user. For example, the comparison table is displayed in a table format or a graph format. Step 4: The acquisition unit acquires information from the email sent when the product is purchased. For example, the acquisition unit accesses the user's email account and acquires product information from the purchase confirmation email, etc. Step 5: The display unit visually displays the monthly expenses by categorizing them based on the information acquired by the acquisition unit. For example, the expenses may be displayed in a pie chart or a bar graph, allowing the user to grasp the status of expenses at a glance. Step 6: The notification section monitors the budget limit set by the user and notifies the user by alert when the expenditure exceeds the limit. For example, a pop-up notification or an email notification is sent when the budget limit is exceeded. Step 7: The suggestion unit manages the expiration dates of points and coupons held by the user and suggests optimal ways to use them. For example, when the expiration date of points is approaching, the suggestion unit suggests optimal products for using points or stores and products for which coupons can be used.

[0149] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0151] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0152] For example, the collection unit is realized by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect product information from multiple shopping sites on the Internet by the control unit 46A of the smart device 14. The analysis unit, for example, analyzes the information collected by the specific processing unit 290 of the data processing device 12, compares prices and discount information, and identifies the lowest price. The comparison table display unit, for example, displays a comparison table generated by the display 40A of the smart device 14. The acquisition unit, for example, accesses the user's email account by the control unit 46A of the smart device 14 and acquires product information from a purchase confirmation email or the like. The display unit, for example, displays the expenses by category in a graph or chart by the display 40A of the smart device 14. The notification unit, for example, issues a pop-up notification when the budget upper limit is exceeded by the control unit 46A of the smart device 14. The suggestion unit, for example, manages the expiration date of points and coupons by the specific processing unit 290 of the data processing device 12, and suggests the optimal use method. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0153] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0154] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0160] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0161] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0162] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0163] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0164] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0165] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0167] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0168] For example, the collection unit is realized by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect product information from multiple shopping sites on the Internet by the control unit 46A of the smart glasses 214. The analysis unit, for example, analyzes the information collected by the specific processing unit 290 of the data processing device 12, compares prices and discount information, and identifies the lowest price. The comparison table display unit, for example, displays a comparison table generated by the display of the smart glasses 214. The acquisition unit, for example, accesses the user's email account by the control unit 46A of the smart glasses 214 and acquires product information from a purchase confirmation email or the like. The display unit, for example, displays the expenses by category in a graph or chart by the display of the smart glasses 214. The notification unit, for example, issues a pop-up notification when the budget upper limit is exceeded by the control unit 46A of the smart glasses 214. The suggestion unit, for example, manages the expiration date of points and coupons by the specific processing unit 290 of the data processing device 12, and suggests the optimal use method. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0169] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0170] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0171] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0172] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0173] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0175] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0176] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0177] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0178] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0179] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0180] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0181] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0182] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0183] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0184] For example, the collection unit is realized by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit can collect product information from multiple shopping sites on the Internet by the control unit 46A of the headset type terminal 314. The analysis unit, for example, analyzes the information collected by the specific processing unit 290 of the data processing device 12, compares prices and discount information, and identifies the lowest price. The comparison table display unit, for example, displays the comparison table generated by the display 343 of the headset type terminal 314. The acquisition unit, for example, accesses the user's email account by the control unit 46A of the headset type terminal 314 and acquires product information from a purchase confirmation email or the like. The display unit, for example, displays the expenses by category in a graph or chart by the display 343 of the headset type terminal 314. The notification unit, for example, issues a pop-up notification by the control unit 46A of the headset type terminal 314 when the budget upper limit is exceeded. The suggestion unit, for example, manages the expiration date of points and coupons by the specific processing unit 290 of the data processing device 12, and suggests the optimal way to use them. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0185] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0186] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0187] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0188] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0189] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0190] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0191] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0192] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0193] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0194] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0195] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0196] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0197] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0198] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0199] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0200] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0201] For example, the collection unit is realized by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect product information from multiple shopping sites on the Internet by the control unit 46A of the robot 414. The analysis unit, for example, analyzes the information collected by the specific processing unit 290 of the data processing device 12, compares prices and discount information, and identifies the lowest price. The comparison table display unit, for example, displays a comparison table generated by the display of the robot 414. The acquisition unit, for example, accesses the user's email account by the control unit 46A of the robot 414 and acquires product information from a purchase confirmation email or the like. The display unit, for example, displays the expenses by category in a graph or chart by the display of the robot 414. The notification unit, for example, issues a pop-up notification when the budget upper limit is exceeded by the control unit 46A of the robot 414. The suggestion unit, for example, manages the expiration date of points and coupons by the specific processing unit 290 of the data processing device 12, and suggests the optimal way to use them. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0202] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0203] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0204] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0205] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0206] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0207] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0208] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0209] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0210] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0211] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0212] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0213] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0214] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0215] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0216] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0217] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0218] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0219] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0220] (Appendix 1) a collection unit that collects price or discount information from various sites; an analysis unit that analyzes the information collected by the collection unit and generates a comparison table; a comparison table display unit that displays a comparison table generated by the analysis unit; An acquisition unit that acquires information from an email when a product is purchased; a display unit that visually displays monthly expenses by categorizing them based on the information acquired by the acquisition unit; A notification section that monitors the budget limit set by the user and sends an alert when the limit is exceeded; A proposal unit that proposes how to manage and utilize the expiration date of points or coupons. A system characterized by: (Appendix 2) The collecting unit includes: Collect product information from multiple online shopping sites 2. The system of claim 1, (Appendix 3) The analysis unit is Analyze the collected information and compare prices and discounts to identify the best deals 2. The system of claim 1, (Appendix 4) The acquisition unit is Access the user's email account and obtain product information from purchase confirmation emails, etc. 2. The system of claim 1, (Appendix 5) The display unit is Display expenses by category in a graph or chart to help users understand their spending at a glance 2. The system of claim 1, (Appendix 6) The notification unit is Monitor user-set budget limits and receive alerts when spending exceeds those limits 2. The system of claim 1, (Appendix 7) The suggestion unit, Manage the expiration dates of points and coupons held by users and suggest appropriate ways to use them 2. The system of claim 1, (Appendix 8) The collecting unit includes: Estimate user sentiment and prioritize sites to collect information based on the estimated user sentiment. 2. The system of claim 1, (Appendix 9) The collecting unit includes: Analyze the user's past purchase history and select the optimal collection method 2. The system of claim 1, (Appendix 10) The collecting unit includes: At the time of collection, filtering is performed based on the user's current purchasing intent 2. The system of claim 1, (Appendix 11) The collecting unit includes: Estimate user emotions and prioritize products to be collected based on the estimated user emotions. 2. The system of claim 1, (Appendix 12) The collecting unit includes: When collecting information, the data is collected in a way that prioritizes the collection of information that is most relevant to the user, taking into account the user's geographic location. 2. The system of claim 1, (Appendix 13) The collecting unit includes: At the time of collection, we analyze your social media activity and collect related information. 2. The system of claim 1, (Appendix 14) The analysis unit is Estimate user emotions and adjust analysis accuracy based on the estimated user emotions 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, adjust the level of analysis detail based on the importance of the product 2. The system of claim 1, (Appendix 16) The analysis unit is During parsing, apply different parsing algorithms depending on the product category 2. The system of claim 1, (Appendix 17) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, prioritize analysis based on when the product was submitted 2. The system of claim 1, (Appendix 19) The analysis unit is During parsing, adjust the parsing order based on product relevance 2. The system of claim 1, (Appendix 20) The comparison table display unit Infer user sentiment and adjust the way comparison tables are displayed based on the inferred user sentiment 2. The system of claim 1, (Appendix 21) The comparison table display unit When displaying a comparison table, the optimal display method is selected by referring to the user's past browsing history. 2. The system of claim 1, (Appendix 22) The comparison table display unit Apply different display formats to different product categories when viewing comparison tables 2. The system of claim 1, (Appendix 23) The comparison table display unit Infer user sentiment and adjust the display order of the comparison table based on the inferred user sentiment 2. The system of claim 1, (Appendix 24) The comparison table display unit When displaying a comparison table, the optimal display method is selected taking into account the user's device information. 2. The system of claim 1, (Appendix 25) The comparison table display unit When displaying comparison tables, the system takes into account the user's geographic location to prioritize the most relevant information. 2. The system of claim 1, (Appendix 26) The acquisition unit is To estimate a user's emotions and adjust the timing of information acquisition from emails based on the estimated user's emotions 2. The system of claim 1, (Appendix 27) The acquisition unit is Analyze the user's past email history and select the optimal retrieval method 2. The system of claim 1, (Appendix 28) The acquisition unit is When retrieving information from email, filter it based on the user's current purchasing intent 2. The system of claim 1, (Appendix 29) The acquisition unit is Infer user sentiment and prioritize emails to be retrieved based on the inferred user sentiment 2. The system of claim 1, (Appendix 30) The acquisition unit is When retrieving information from emails, the system takes into account the user's geographic location to prioritize retrieving information that is more relevant 2. The system of claim 1, (Appendix 31) The acquisition unit is Analyze users' social media activity and retrieve relevant information when retrieving information from emails 2. The system of claim 1, (Appendix 32) The display unit is Inferring user sentiment and adjusting how expenses are displayed based on the inferred user sentiment 2. The system of claim 1, (Appendix 33) The display unit is When displaying expenses, select the optimal display method by referring to the user's past expense history. 2. The system of claim 1, (Appendix 34) The display unit is Apply different display formats to different categories when viewing expenses 2. The system of claim 1, (Appendix 35) The display unit is Inferring a user's emotions and adjusting the display order of expenses based on the inferred user emotions 2. The system of claim 1, (Appendix 36) The display unit is When displaying expenses, select the optimal display method taking into account the user's device information 2. The system of claim 1, (Appendix 37) The display unit is When displaying expenses, the app takes into account the user's geographic location to prioritize the most relevant information. 2. The system of claim 1, (Appendix 38) The notification unit is Infer user emotions and adjust how alerts are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 39) The notification unit is When an alert is sent, the system will select the most appropriate notification method by referring to the user's past budget overage history. 2. The system of claim 1, (Appendix 40) The notification unit is When alerting, apply different notification formats depending on the cause of the budget overage 2. The system of claim 1, (Appendix 41) The notification unit is Infer user emotions and prioritize alerts based on the inferred user emotions 2. The system of claim 1, (Appendix 42) The notification unit is When an alert is sent, the optimal notification method is selected based on the user's device information. 2. The system of claim 1, (Appendix 43) The notification unit is When receiving alerts, the system takes into account the user's geographic location to prioritize the most relevant information. 2. The system of claim 1, (Appendix 44) The suggestion unit, Infer user emotions and adjust the presentation of suggestions based on the inferred user emotions 2. The system of claim 1, (Appendix 45) The suggestion unit, When making a proposal, adjust the detail of the proposal based on the importance of points or coupons 2. The system of claim 1, (Appendix 46) The suggestion unit, When making suggestions, apply different suggestion algorithms depending on the points or coupon category. 2. The system of claim 1, (Appendix 47) The suggestion unit, Infer user sentiment and adjust the length of suggestions based on the estimated user sentiment 2. The system of claim 1, (Appendix 48) The suggestion unit, When making offers, prioritize offers based on points or coupon expiration dates 2. The system of claim 1, (Appendix 49) The suggestion unit, When making suggestions, adjust the order of suggestions based on points or coupon relevance 2. The system of claim 1, [Explanation of symbols]

[0221] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects price or discount information from various sites; an analysis unit that analyzes the information collected by the collection unit and generates a comparison table; a comparison table display unit that displays a comparison table generated by the analysis unit; An acquisition unit that acquires information from an email when a product is purchased; a display unit that visually displays monthly expenses by categorizing them based on the information acquired by the acquisition unit; A notification section that monitors the budget limit set by the user and sends an alert when the limit is exceeded; A proposal unit that proposes how to manage and utilize the expiration date of points or coupons. A system characterized by:

2. The collecting unit includes: Collect product information from multiple online shopping sites 2. The system of claim 1 .

3. The analysis unit is Analyze the collected information and compare prices and discounts to identify the best deals 2. The system of claim 1 .

4. The acquisition unit is Access the user's email account and obtain product information from purchase confirmation emails, etc.

2. The system of claim 1 .

5. The display unit is Display expenses by category in a graph or chart to help users understand their spending at a glance 2. The system of claim 1 .

6. The notification unit is Monitor user-set budget limits and receive alerts when spending exceeds those limits 2. The system of claim 1 .

7. The suggestion unit, Manage the expiration dates of points and coupons held by users and suggest appropriate ways to use them 2. The system of claim 1 .

8. The collecting unit includes: Estimate user sentiment and prioritize sites to collect information based on the estimated user sentiment.

2. The system of claim 1 .

9. The collecting unit includes: Analyze the user's past purchase history and select the optimal collection method 2. The system of claim 1 .

10. The collecting unit includes: At the time of collection, filtering is performed based on the user's current purchasing intent 2. The system of claim 1 .

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